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Stochastic gene expression and its consequences

Arjun Raj and Alexander van Oudenaarden*

Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA

Abstract

Gene expression is a fundamentally stochastic process, with randomness in transcription and

translation leading to significant cell-to-cell variations in mRNA and protein levels. This variation

appears in organisms ranging from microbes to metazoans and its characteristics depend both on

the biophysical parameters governing gene expression and on gene network structure. Stochastic

gene expression can have important consequences for cellular function, being beneficial in some

contexts and harmful in others. These situations include stress response, pathogenesis,

metabolism, development, cell cycle, circadian rhythms and aging.

Introduction

Life is a study in contrasts between randomness and determinism: from the chaos of

biomolecular interactions to the precise coordination of development, living organisms are

able to resolve these two seemingly contradictory aspects of their internal workings.

Scientists often reconcile the stochastic and the deterministic by appealing to the statistics of

large numbers, thus diminishing the importance of any one molecule in particular. However,

cellular function often involves small numbers of molecules, of which perhaps the most

important example is DNA. It is this molecule, usually present in just one or few copies per

cell, that gives organisms their unique genetic identity. But what about genetically identical

organisms grown in homogenous environments? To what degree are they unique?

Increasingly, researchers have found that even genetically identical individuals can be very

different, and that some of the most striking sources of this variability are random

fluctuations in the expression of individual genes. Fundamentally, this is because the

expression of a gene involves the discrete and inherently random biochemical reactions

involved in the production of mRNAs and proteins. The fact that DNA (and hence the genes

encoded therein) is present in very low numbers means that these fluctuations do not just

average away but can instead lead to easily detectable differences between otherwise

identical cells; in other words, gene expression must be thought of as a stochastic process.

The experimental observation that the levels of gene expression vary from cell-to-cell is

certainly not new. In 1957, Novick and Weiner showed that the production of beta-

galactosidase in individual cells was highly variable and random, with induction increasing

the proportion of cells expressing the enzyme rather than increasing every cell's expression

level equally (Novick and Weiner, 1957). Such early studies were hindered, however, by thelack of reliable single-cell assays of gene expression. One of the first studies to use an

expression reporter in single-cells to examine the stochastic underpinnings of expression

variability is the pioneering work of Ko et al., 1990. They examined the effect of different

doses of glucocorticoid on the expression of a glucocorticoid-responsive transgene encoding

beta-galactosidase and found that the cell-to-cell variability in the expression of the

transgene was surprising large. Moreover, increasing the dose led to an increased frequency

*Corresponding author: [email protected]; 617-253-4446.

NIH Public AccessAuthor ManuscriptCell. Author manuscript; available in PMC 2011 June 18.

Published in final edited form as:

Cell. 2008 October 17; 135(2): 216226. doi:10.1016/j.cell.2008.09.050.

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of cells displaying a high level of expression rather than a uniform increase in expression in

every cell; that is, dose dependence was a consequence of changing the probability that an

individual cell would express the gene at a high level.

Yet despite the potential biological consequences of random cellular variability (Spudich

and Koshland, 1976), several years would pass before theoretical work ignited much of the

present interest in stochastic gene expression (McAdams and Arkin, 1997; Arkin et al. 1998)

They modeled gene expression using a stochastic formulation of chemical kinetics derivedby Gillespie (1977), predicting that in some biologically realistic parameter ranges, protein

numbers could fluctuate markedly within individual cells. Theythen extended their analysis

to model the circuit underlying the decision between lysis and lysogeny of the phage-

lambda, showing that stochastic effects in the expression of key regulators could explain

why some cells activated the lytic pathway whereas others followed the lysogenic pathway.

The notion that stochastic effects in gene expression could have important biological

implications has motivated much research in the field and has only recently been explored

experimentally.

Since this early research, the study of stochastic gene expression has blossomed into a rich

field, with researchers from a diverse set of backgrounds working on a wide range of

problems. The field is also notable for its strong interplay between theory and experiment,

with many scientists making significant contributions to both. In this review, we willdescribe these researchers' efforts to characterize the underlying phenomenon through a host

of organisms using a variety of experimental and theoretical methods. We will then

highlight some recent endeavors trying to tie stochastic gene expression to biological

phenomena.

Noisy bugs

The first attempts to characterize stochastic gene expression were born from experiments in

synthetic biology in which experimenters found that noisy behavior in gene expression was

interfering with the operation of engineered genetic circuits. One example is the

repressilator, a synthetic network of repressors that was capable of producing oscillations

in gene expression (Elowitz and Leibler, 2000). The authors found that the oscillations were

subject to marked fluctuations in their period and magnitude, and conjectured that stochasticeffects in gene expression were causing these effects. In another study explicitly aimed at

controlling fluctuations, Becskei and Serrano (2000) showed that engineering a circuit with

negative feedback could reduce cell-to-cell variability in expression. Although these

experiments showed that noise in gene expression was important and could even be

controlled, the molecular basis for the observed variability remained unclear.

The first experiments to explore the causes of stochastic gene expression were the landmark

studies of Elowitz et al. (2002) and Ozbudak et al. (2002). Elowitz et al. introduced the

concepts of extrinsic and intrinsic noise in gene expression (analyzed mathematically by

Swain et al., 2002). These ideas are perhaps most easily explained through example. In their

experiments, Elowitz et al. quantified the variability in the expression from a promoter inE.

coli by introducing two copies of the same promoter into the genome ofE. coli, one driving

the expression of cyan fluorescent protein (CFP) and the other driving the expression ofyellow fluorescent protein (YFP) (Figures 1A and 1B). In this setup, extrinsic fluctuations

are those that affect the expression of both copies of the gene equally in a given cell, such as

variations in the numbers of RNA polymerases or ribosomes. Intrinsic fluctuations are those

due to the randomness inherent to transcription and translation; being random, they should

affect each copy of the gene independently, adding uncorrelated variations in levels of CFP

and YFP levels (Figure 1C). They found that both sources of noise can be significant

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depending on the promoter. Later time-lapse measurements showed that in bacteria, the time

scale for intrinsic fluctuations is less than 9 minutes, whereas extrinsic fluctuations exert

their effects on time scales of about 40 minutes, or roughly the length of the cell cycle

(Rosenfeld et al., 2005).

Ozbudak et al. (2002) obsereved that variability in the expression of a gene expressing GFP

driven by an inducible promoter inB. subtilis depended on the underlying biochemical rates

of transcription and translation. In these experiments transcription rates were controlled byvarying the level of induction and the translation rate was altered by introducing mutations

into the ribosomal binding site. This verified a stochastic theory of intrinsic noise they had

developed predicting how noise in gene expression would change as these parameters were

altered (Thattai and van Oudenaarden, 2001) (Figures 2A and 2B). In particular, the theory

predicted that noise (measured by the standard deviation in protein expression level divided

by the mean) would depend inversely on the rate of transcription but would not depend on

the rate of translation. This is because proteins are produced in translational bursts from

individual transcripts; the concept of bursts in gene expression continues to play an

important role in current research, especially in higher eukaryotes.

Recently, a set of exciting single-molecule experiments have observed translational bursts in

individual living bacteria. To count the number of proteins per cell, Cai et al., 2006 used

used two methods: one involving microfluidics, in which they quantified the number ofbeta-galactosidase enzymes in a cell by monitoring its enzymatic activity, and one involving

direct visualization of single YFP molecules tethered to the cellular membrane (Yu et al.,

2006). Both studies showed that proteins were synthesized in rapid, burst-like fashion.

Another study (Golding et al., 2005) used the MS2-GFP method (Bertrand et al., 1998;

Beach et al., 1999), which allows one to monitor the transcription of individual mRNA

molecules in real time. This is accomplished by introducing a repeated sequence motif into

the 3 untranslated region of the mRNA to which a fusion of the MS2 coat protein and GFP

binds, thus rendering the mRNA molecule fluorescent. According to the model presented in

Figure 3A, one would expect that mRNA molecules are produced at a steady rate according

to the statistics of a Poisson process. The authors found, however, that the mRNA molecules

were themselves produced in transcriptional bursts, as if the gene itself was randomly

switching back and forth between transcriptionally active and inactive states. This findingmirrors results obtained for eukaryotes described below. It would be interesting to combine

these different measurements of the dynamics of individual mRNAs and proteins, given the

role that competition between translation and mRNA degradation may play in stochastic

gene expression (Yarchuk et al., 1998).

Eukaryotes and the burst hypothesis

After these experiments in bacteria, researchers began to investigate stochastic gene

expression in eukaryotes, initially focusing on yeast. Almost immediately, several reports

seemed to indicate that the sources of variability in gene expression in yeast are different

from those in bacteria in a number of important ways (Becskei et al., 2005; Blake et al.,

2006; Blake et al., 2003; Raser and O'Shea, 2004). These studies all examined the

relationship between the mean level of expression and the variation about that mean, arelationship that is in theory qualitatively different depending on the sources of noise. In all

these studies, the relationship predicted by the simple model in Figure 2 was insufficient to

explain the experimental observations. These observations were, however, compatible with

models of transcriptional bursts in which the gene itself randomly transitioned between

states of transcriptional activity and inactivity (Figure 3B). Such models of transcriptional

bursting add another important source of stochasticity beyond random events in transcription



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and translation, which have now been analyzed theoretically in some detail (Friedman et al.,

2006; Karmakar and Bose, 2004; Kepler and Elston, 2001; Pedraza and Paulsson, 2008).

That such models are required to explain eukaryotic data but not most prokaryotic data (Cai

et al., 2006; Maamar et al., 2007; Yu et al., 2006), with an important exception (Golding et

al., 2005), strongly suggests that some regulator of gene expression specific to eukaryotes is

responsible. The most likely candidate for this is remodeling of chromatin: when the

surrounding chromatin is in an open, acetylated state, the gene is able to transcribe relativelyfreely, whereas when chromatin is in a condensed state, transcription is repressed. Although

there is still no direct evidence that chromatin remodeling is responsible for stochastic

changes in gene activity, several studies have tried to link chromatin-related events to

stochastic gene expression by indirect means. These include positional effects like

measuring correlations between proximally located genes (Becskei et al., 2005; Raj et al.,

2006) or altering the behavior of chromatin remodeling agents (Raser and O'Shea, 2004; Xu

et al., 2006). However, global studies of noise in yeast (Bar-Even et al., 2006; Newman et

al., 2006) have shown that the presence of chromatin remodeling complexes is neither

necessary nor sufficient for the expression of a gene to be noisy; also, factors such as the

location and number of transcription factor binding sites can control noise (Murphy et al.,

2007).

In yeast noise in gene expression is primarily extrinsic in origin (Becskei et al., 2005;Colman-Lerner et al., 2005; Raser and O'Shea, 2004; Volfson et al., 2006), resulting in

correlated fluctuations between different genes. Sources identified thus far for this extrinsic

noise are cell size (Raser and O'Shea, 2004; Newman et al., 2006; Volfson et al., 2006),

variations in common upstream factors (Becskei et al., 2005; Volfson et al., 2006) and

chromosomal location (Becskei et al., 2005); by contrast, extrinsic variability in prokaryotic

gene expression is thought to stem mostly from variations in upstream factors (Elowitz et

al., 2002). There is some debate as to the role of differences in cell-cycle and cell size, with

some data (Raser and O'Shea, 2004) showing that extrinsic variability remains even after

controlling for these variables, whereas other data indicates that a stringent analysis of size

and shape by flow cytometry can account for most of the extrinsic noise (Newman et al.,

2006). Generally, one of the difficulties in studying extrinsic variability is its catchall nature:

the lack of any specific mechanism makes its analysis rather phenomenological. Although

there is some knowledge of the time scales over which extrinsic noise operates (Rosenfeld etal., 2005) and theoretical analyses of the effects that it might have (Shahrezaei et al., 2008)

(Paulsson, 2004), understanding extrinsic noise remains an unresolved problem in the field.

Higher eukaryotes: noisier than expected

Meanwhile, work has begun on systematically examining cell-to-cell variability in gene

expression in higher eukaryotes. A priori, one might expect that higher eukaryotes, with

their larger size and numbers of molecules, might exhibit less variability than prokaryotes

and yeast. On the other hand, the prevalence of transcriptionally-silenced heterochromatin

would argue that slow, random events of gene activation and inactivation would lead to

much larger fluctuations than in unicellular organisms. As it happens, the latter is the case,

with a growing body of evidence that fluctuations in higher eukaryotes can be remarkably

large.

Interestingly, the study of expression variability in higher eukaryotes began well before the

recent heightened interest in stochastic gene expression. Beginning with the aforementioned

work of Ko et al. (1990), several other reports indicated that gene expression in mammalian

cells was variable, stemming from short, rare events of active transcription (Ross et al.,

1994; Newlands et al., 1998; Takasuka et al., 1998; White et al., 1995).



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Many of these early experiments were limited by the difficulties inherent to measuring gene

expression in single-cells in higher eukaryotes. One problem is sensitivity: owing to their

large cellular volumes, even moderately expressed fluorescent proteins can be difficult to

detect. Another problem is the lack of tools available to manipulate these organisms

genetically. To circumvent these problems, researchers have come up with many new ways

of assaying gene expression at the single-cell level to measure cell-to-cell variability.

One approach is to measure mRNAs rather than proteins. For instance, utilizing the MS2-GFP method of mRNA detection (Beach et al., 1999; Bertrand et al., 1998), Chubb et al.

(2006) showed that a developmental gene inDictyostelium discoideum is transcribed in a

pulsatile fashion, directly demonstrating the burst hypothesis by watching mRNAs

accumulate and dissipate from active and inactive sites of transcription in real time. In

comparison with the less intense bursts observed using a similar approach in bacteria

(Golding et al., 2005), the authors found that the bursts were less frequent but longer lasting.

In contrast with earlier bacterial models, this shows that bursts in gene expression are the

primary intrinsic cause of cell-to-cell variability.

One can also measure mRNA numbers in single cells across a population using variants of

fluorescence in situ hybridization (FISH) capable of detecting individual mRNA molecules

(Femino et al., 1998; Raj et al., 2006; Raj et al., 2008). Raj et al. (2006) combined single

molecule FISH with statistical analysis to show that individual mammalian cells transcribeda stably integrated transgene in infrequent but potent bursts, resulting in large cell-to-cell

variations in mRNA number (Figures 3C and 3D) that correlated with the presence or

absence of active sites of transcription [seen also by (Voss et al., 2006)]. These bursts were

correlated between genes that were located proximally to each other but not between genes

that were distally located, providing another clue that chromatin remodeling may be

responsible for genes transitioning between an active and inactive state: opening of the

chromatin surrounding one gene is likely to open chromatin for neighboring genes, leading

to correlations in their expression, whereas distant genes are not affected in this coordinated

manner, resulting in uncorrelated expression. This behavior is also seen in globin expression

(de Krom et al., 2002) and shows that genomic position can be important in interpreting the

concepts of intrinsic and extrinsic noise.

Quantitative single-cell RT-PCR methods have been used to obtain cell-by-cell counts ofendogenous mRNAs, thus circumventing issues associated with generating transgenic cell

lines and organisms. By simultaneously measuring the numbers of five transcripts in

individual pancreatic islet cells, Bengtsson et al. (2005) showed that the distributions of

these mRNAs across the population were heavily skewed as in Figure 3D. Moreover, they

measured correlations in the fluctuations in the expression of these genes, finding that two

functionally related genes were highly correlated whereas the rest were uncorrelated,

perhaps pointing to the existence of common regulators for the two genes. Such findings

highlight the potential use of stochastic gene expression in uncovering the mechanisms of

transcriptional regulation. One difficulty with this approach is the rigorous set of controls

required to calibrate RT-PCR results in molecular units, a problem that can be obviated

through the use of so-called digital RT-PCR. This method, in which cDNA reverse

transcribed from an individual cell is fractionated into enough individual PCR reactions that

each reaction will contain either 0 or 1 cDNAs, has been used to examine the expression ofthe PU.1 transcription factor in both hematopoetic stem cells and in myeloid progenitor cells

(Warren et al., 2006), in which the authors observed marked heterogeneity in transcript

levels.

Atlhough the evidence for transcriptional bursting continues to accumulate, little is known

about the source of these bursts. As mentioned earlier, one possibility often posited is that



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stochastic events of chromatin remodeling could underlie the bursts by causing the gene to

switch between transcriptionally active and inactive states (Becskei et al., 2005; Raj et al.,

2006; Raser and O'Shea, 2004; Warren et al., 2006). In support of this view, direct

visualization of chromatin remodeling has shown it to be a slow process that can act over a

long range on a timescale of hours (Tumbar et al., 1999). However, there are other plausible

mechanisms that might underlie transcriptional bursts. One possibility is the existence of

pre-initiation complexes that form on the promoter region of the DNA and facilitate multiple

rounds of RNA polymerase II transcription events (Blake et al., 2006; Blake et al., 2003). Ifsuch complexes exist only for short periods of time, they could also result in pulsatile

transcription. Another point to consider is that transcription doesn't take place in a uniform

fashion throughout the genome but is concentrated in transcriptional factories (Jackson et

al., 1993; Wansink et al., 1993) to which active genes are recruited (Osborne et al., 2004).

Remarkably, it appears that a limited number of these factories (on the order of hundreds)

are responsible for most mRNA transcription in the cell; thus, competition for these factories

could result in the stochastic expression of any given gene. Ultimately, understanding the

biochemical origins of bursting may require the application of new (or perhaps combinations

of old) techniques for imaging gene expression and genome organization in real-time, as

cell-to-cell variability in population snapshots may not be sufficient to resolve the

dynamics of the bursting mechanisms (Pedraza and Paulsson, 2008). Although difficult, the

prize for such a technical feat would be a much deeper understanding of the transcriptional

process.

The above studies examining mRNA copy number variation provide insights into the origins

of noise, although they mostly fail to show how those mRNA fluctuations propagate to noise

in protein levels. To examine noise in protein levels in human cells, Sigal et al. (2006) used

a clever strategy to fluorescently tag endogenous proteins. They transfected a cell line with

DNA containing artificial YFP exons that occasionally insert themselves into an intron, YFP

is included in the protein encoded by the encapsulating gene. Using time-lapse microscopy,

the authors were able to show that gene expression in individual cells was variable, but that

the fluctuations were slowly varying in time; that is, it took multiple cell divisions before a

highly expressing cell would become a lowly expressing cell and vice versa. Interestingly,

they also found correlations between genes in the same pathway, but not between unrelated

genes, echoing the results of Bengtsson et al. (2005).

Yet, the variability observed at the protein level by Sigal et al. (2006) seems generally much

smaller than that observed at the mRNA level in the aforementioned studies, with the

distribution of mRNAs being much more heavily skewed (Figure 3B). How might such a

discrepancy be resolved? One answer may be methodological: by screening for cells

expressing a detectable amount of YFP, the proteins with YFP insertions obtained by Sigal

et al. may be biased towards heavily or constitutively expressing genes with less variability,

an interpretation supported by the fact that variability in the number of GAPDH mRNAs is

lower than other genes (Warren et al., 2006). It is also possible that protein stability plays a

role in the relationship between mRNA and protein variability (Raj et al., 2006). Short-lived

proteins will track mRNA levels very closely, leading to protein distributions that resemble

(and correlate strongly with) mRNA distributions. However, if the proteins degrade slowly

(as is the case for YFP), then the large pool of older proteins will buffer the rapid

fluctuations in mRNA; that is, mRNA bursts may serve only to top up protein levels. Inthis case, mRNA and protein levels do not strongly correlate.

Networked noise

Building on these studies elucidating the sources and characteristics of noise, researchers

went on to study the effects of noise in simple synthetic genetic networks. One example is



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transcriptional cascades, which are a common regulatory motif, particularly in development.

First, researchers investigated the effect of noise in an upstream gene on noise in a

downstream gene. This was done using multiple fluorescent reporters to quantify the relative

contributions of variability in the upstream gene, global noise due to effects such as cell size,

and also noise intrinsic to the expression of the downstream gene (Pedraza and van

Oudenaarden, 2005; Rosenfeld et al., 2005). They found that variability can be transmitted

from the upstream gene to the downstream gene, adding substantially to the noise inherent in

downstream gene's expression. Further study of cascades showed that longer geneticcascades can filter out rapid fluctuations at the expense of amplifying noise in the timing of

the propagated signal (Hooshangi et al., 2005). Mathematical analysis has also shown that

stochastic behavior can have the counterintuitive effect of actually lowering transmitted

variability (Paulsson and Ehrenberg, 2000; Thattai and van Oudenaarden, 2002).

Negative or positive feedback are other very common types of regulation in genetic

networks. In these types of feedback loops the protein encoded by a gene negatively or

positively influences its own transcription. Negative feedback can reduce the effects of noise

because fluctuations above and below the mean are pushed back towards the mean, as has

been predicted theoretically (Savageau, 1974; Thattai and van Oudenaarden, 2001) and

demonstrated experimentally (Austin et al., 2006; Becskei and Serrano, 2000; Dublanche et

al., 2006).

In the presence of positive feedback, noise can result in much more dramatic behavior.

Positive feedback can act as a switch, in which a small amount of expression from a given

gene can serve to further activate expression of the gene itself, eventually flipping the gene

from an off state to an on state. In the presence of cooperativity, though, a cell can

remain in the off state indefinitely, as cooperativity creates a threshold that the protein

level must surpass in order to trigger the feedback. In that case, occasional large fluctuations

in gene expression can serve to randomly activate the switch and push the cell into the on

state (Hasty et al., 2000). This bistable expression pattern has been observed in synthetic

systems with positive feedback switches (Becskei et al., 2001; Gardner et al., 2000; Isaacs et

al., 2003; Kramer and Fussenegger, 2005) and also in several naturally occurring genetic

positive feedback loops (Acar et al., 2005; Maamar and Dubnau, 2005; Maamar et al., 2007;

Suel et al., 2006; Suel et al., 2007; Smits et al., 2005). The existence of multiple phenotypic

profiles also appears in more complex biological networks, as we shall see in the nextsection.

Noise in its natural context

Researchers have only recently begun to explore the role fluctuations play in biological

situations. One can imagine two roles for noise in cellular function: one is as a nuisance that

serves as an impediment to reliable behavior, and one is as a source of variability that cells

may exploit. In the remainder of the review, we first focus on cases where noise is beneficial

and then discuss the potential negative effects of noise, drawing on examples in organisms

ranging from microbes to metazoans.

Useful unicellular variability

In unicellular organisms, one can make the argument that variability could be very useful inthat it would allow heterogenous phenotypes even in clonal populations, enabling a

population of organisms to commit certain subpopulations to different behaviors.

Variability in a population is enhanced by networks that can produce multiple, mutually

exclusive profiles of gene expression profiles (such as ON and OFF expression of a

particular gene) within single organisms. These states are bistable (or multistable) in the

sense that small variations in expression are insufficient to cause the organism to flip from



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one state to another and are often heritable, providing a mechanism for epigenetic

inheritance (Ptashne, 2007). Occasionally, however, a large stochastic fluctuation in gene

expression can induce a transition from one state to another, an idea that underlies many of

the following studies.

MetabolismMetabolic networks are an important and perhaps surprising class of geneticnetworks exhibiting multistability with stochastic transitions. Despite being some of the

most extensively studied gene networks in existence, only recently have researchers begunto examine the behavior of metabolic genes at the single-cell level, yielding unanticipated

results. For instance, following up on the pioneering studies of Novick and Weiner, it was

found that the lactose utilization network inE. coli displayed an all or none type of

behavior and that single cells stochastically transition between these two states (Mettetal et

al., 2006; Ozbudak et al., 2004). Such behavior has also been seen in cells that were all

initially in an uninduced state, arguing that some stochastic mechanism must have caused

the network to switch from the off to the on expression state. In another example the

galactose utilization network in yeast also displays strikingly bimodal patterns in the

expression of the GAL family of genes responsible for galactose metabolism (Acar et al.,

2005). The authors explained this using a model in which fluctuations in the GAL3 gene

were responsible for transitions between the ON and OFF states. Then they altered the

expression of a key feedback component of the network, thereby changing the degree to

which the fluctuations were buffered and thus modulating the frequency of the stochastictransitions. The dynamics of these switching events has also been analyzed using time-lapse

microscopy, yielding fascinating results (Kaufmann et al., 2007). There, the authors found

that not only were the states themselves heritable, but the transition itself was heritable in

that related yeast cells appeared to switch in a correlated fashion. Again, the authors were

able to explain their results using a stochastic model, with the key feature being stochastic

bursts in GAL80 expression. In all of these studies, however, the link between stochastic

switching and stochastic gene expression has been implicit rather than explicit, with more

experiments being required to validate the models.

Of course, these results raise the inevitable question of why genetically identical populations

would display such marked phenotypic variability in their metabolic pathways. One idea is

that having individual cells stochastically switch between activating or inactivating a

metabolic pathway could confer a fitness advantage to the overall population in fluctuatingenvironments (Kussell and Leibler, 2005; Thattai and van Oudenaarden, 2004; Wolf et al.,

2005). Intuitively, this benefit arises from a tradeoff between anticipation and sensing of

food sources. Cells can either directly sense food in the environment before activating their

metabolic networks, or they can choose to stochastically commit some fraction of the

population to having those metabolic networks active in anticipation of the arrival of a new

food source. The cost of the former strategy is slow response time and implementation of the

sensing apparatus, whereas the latter strategy essentially sacrifices some fraction of the

population to suboptimal growth. These studies have shown that stochastic switching is a

viable alternative to sensing and that it is most effective when the switching rate is closely

tuned to the rate at which the environment fluctuates. Experimentally, Acar et al. (2008)

tested these theories by monitoring the growth rate of a yeast strain with a controllable rate

of switching in a periodically fluctuating environment and show that fast switchers do

indeed grow faster in rapidly fluctuating environments whereas slow switchers do better

when environmental changes come more slowly. Furthermore, Blake et al. (2006) showed

that expression variability, even in the absence of discrete fit and unfit expression states, can

be beneficial in times of stress. It is likely, however, that in real biological systems, cells

rely on some combination of variability in gene expression and sensing in their stress

responses; elucidating this interplay in biological contexts could have broad implications for

microbial growth strategies.



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Microbial stress responsesAnother case of bet-hedging in microbial populations is

in the response to cellular stress, such as lack of food or exposure to antibiotics. A

particularly nice example of the former that has garnered considerable recent attention is the

phenomenon of competence inB. subtilis. B. subtilis has the remarkable ability to take up

DNA from the environment (called competentence), which exhibits itself upon the entry to

stationary phase through the activation of a quorum sensing mechanism. Interestingly, only

a small fraction (roughly 10-20%) of the cells become competent, while the rest remain in a

vegetative state. This phenotypic variability was first observed over 40 years ago (Nesterand Stocker, 1963) and is the result of a positive feedback loop in which the transcription

factor ComK promotes its own expression: when the feedback loop is activated, high levels

of ComK are produced, activating a host of downstream genes involved in DNA uptake,

whereas non-competent cells produce only low basal amounts of ComK (Maamar and

Dubnau, 2005; Smits et al., 2005; Suel et al., 2006). The resulting bimodal expression

pattern is easily visualized using fluorescent proteins.

A natural hypothesis is that spontaneous fluctuations in comKexpression of sufficient

magnitude can cause a non-competent cell to transition to competence. To test this notion,

Maamar et al. (2007) quantified comKexpression in individual non-competent cells by using

single-molecule FISH to count the number ofcomKtranscripts. They showed that increasing

the mean level ofcomKtranscription resulted in an increase in the percentage of competent

cells, presumably because the fraction of cells with fluctuations in ComK above a certainthreshold also increased. To test that possibility directly, they increased the comK

transcription rate while lowering the translation rate, which reduces noise in gene expression

while leaving the mean expression level unchanged (Ozbudak et al., 2002; Thattai and van

Oudenaarden, 2001). Lowering the noise should reduce the number of cells whose

fluctuations cross the threshold for competence, and indeed the authors found that the

number of competent cells was dramatically reduced, demonstrating the importance and

utility of noise theory in biological situations. Another recent study (Suel et al., 2007)

showed that reducing total cellular noise also resulted in a lower percentage of competent

cells. To achieve this overall noise reduction, they used a special mutant that is unable to

septate, resulting in very large cells with multiple genomes. In these large cells, the impact

of all fluctuations is reduced, given that the cell is in some ways the average of many

smaller cells, with ever larger cells consequently having ever lower overall fluctuations. The

authors found that these larger cells did in fact display commensurately fewer transitions tothe competent state. Overall, though, it is important to note that the low number of comK

transcripts measured (Maamar et al., 2007) and the non-uniformity of the duration of

competence episodes (Suel et al., 2007) imply that this system has evolved to be

purposefully imprecise, a feature that cells may exploit in other situations.

Stochastic effects coupled with positive feedback can also lead to variability in the timing of

particular molecular events such as the onset of meiosis in yeast (Nachman et al., 2007). In

this work the timing between introduction of environmental stress and the onset of meiosis

in individual cells was highly variable. This variability seemed not to depend on position in

the cell cycle or other external factors, but rather was heavily dependent on noise in the

expression of the meiotic regulator Ime1 (although cell-size did appear to have a strong

effect). Together, these studies paint a picture in which noise in gene expression can lead to

random fates at random times when stressed, a surprising finding that may ultimately proveremarkably prevalent.

PathogensHeterogeneous phenotypes in clonal populations can also be medically

relevant. One example is bacterial persistence in the face of antibiotic exposure. Persistent

cells grow at a much slower rate than non-persistent cells, but are able to survive antibiotic

treatment. The existence of persistent subpopulations ofMycobacterium tuberculosis,



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Staphylococcus aureus, and Pseudomonas aeruginosa among other is thought to be a major

obstacle to effective treatment (Stewart et al., 2003). Interestingly, the work of Balaban et al.

(2004) showed that a small persistent subpopulation exists even in untreated cultures ofE.

coli and that these persistent cells are generated continuously during growth. Although not

much is known about how the underlying network can result in such disparate phenotypes, it

is entirely possible that stochastic gene expression could play a significant role in

establishing non-genetic heterogeneity in these populations.

Another example of heterogeneity in a pathogen is that of the latent phase of HIV infection.

Upon infection with the HIV virus, a small pool of latent CD4 T lymphocytes forms

containing stably integrated but non-expressing virus. The low level of expression of the

virus in this population of cells renders them difficult to target pharmacologically, making

latency a serious impediment to effective treatment. Weinberger et al. (2005) showed that

one explanation for the latent and active expression patterns is a positive feedback loop

mediated by the Tat protein. They showed that stochastic fluctuations in Tat expression can

interact with the feedback loop to create populations of cells with high and low levels of

viral expression Interestingly, though, later work (Weinberger et al., 2007) showed that Tat

positive feedback did not serve to maintain the ON state (as in the competence network in

B. subtilis) but rather that the heterogeneity was caused by large transient bursts of

expression that positive feedback served to amplify rather than stabilize (Weinberger et al.,

2008).

Random Developments

As the above examples demonstrate, there is a clear rationale for using stochastic gene

expression to create a diversity of phenotypes, namely that isogenic populations of viruses,

bacteria and yeast cannot display heterogeneity in any other way. However, in many higher

eukaryotes, population diversity largely arises from genetic and environmental diversity,

making the argument for utilizing stochastic gene expression less plausible. In development,

for example, one would imagine that a deterministic execution of the developmental

program would be critical to producing functional tissues, with organism-to-organism

variations reflecting genetic rather than stochastic differences. Yet even in development,

researchers are finding many interesting examples of stochastic cell-fate decisions linked to

stochastic gene expression.

One celebrated example of stochastic gene expression having an important role in

development is the expression of different odorant receptors in different sensory neurons in

mice. Olfaction presents an interesting regulatory challenge, as there are over a thousand

different odorant receptors, each of which must be expressed differentially in individual

neurons to confer distinctive sensitivity. Developing a regulatory network capable of such

complex decision making is prohibitively complex, so the mouse adopts a much simpler

Monte-Carlo strategy in which each neuron randomly expresses a particular odorant

receptor (Vassar et al., 1993) in a mutually exclusive fashion (Tsuboi et al., 1999). A

fascinating line of further inquiry would be to determine the stochastic mechanisms

responsible for these choices during the development of the olfactory epithelium and

elucidation of the network responsible for locking in a particular decision once made.

Another particularly nice instance in which stochastic gene expression has been explicitly

linked to a cell fate decision is photoreceptor expression in Drosophila eyes. The Drosophila

eye consists of a large number of optical units called ommatidia, each of which contains two

cells that in turn express one of a specific pair of photoreceptors, eitherRh3 andRh5 (for

blue sensitive ommatidia) orRh4 andRh6(for yellow sensitive ommatidia). Wernet et al.

(2006) showed that this decision is almost exclusively due to the stochastic expression of the



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spineless gene during mid-pupation, with stochastically large levels ofspineless expression

resulting in the adoption of the yellow fate in roughly 70% of the ommatidia.

The process of hematopoiesis, in which progenitor stem cells differentiate into the various

types of blood cells, is another example in which cellular differentiation may be stochastic

(Enver et al., 1998; Hume, 2000). To link this stochastic differentiation to variations in gene

expression, Chang et al. (2008) showed that variability in the expression of the stem cell

marker Sca-1 in individual cells correlated strongly with the probability of that cell tochoose an erythroid or myeloid lineage. Moreover, microarray analysis on the populations of

cells expressing high and low levels of Sca-1 showed transcriptome-wide variability,

indicating that the fluctuations were not limited only to a small set of genes. It would be

interesting to see how widespread these massively correlated fluctuations are in other

examples of stochastic differentiation and if these correlations stem from an unknown

master regulator or arise from noise in many parts of a large interlocking genetic network.

Shutting out the noise

Despite these examples of organisms exploiting noise, it is possible, if not probable, that

noise in gene expression is more generally an obstacle that organisms must overcome to

achieve robust function. Less is currently known about the mechanisms by which the effects

of noise are minimized, likely due to the difficulty in studying a phenomenon that by

definition is invariant to perturbations. In fact, much of the focus on the benefits of noise

reflects the fact that studying the consequences of stochastic gene expression is much easier

when the phenomenon in question is itself stochastic. Nevertheless, progress has been made

in understanding how organisms tolerate noise, from the basics of cellular function through

development.

Genomic approaches

One way to find evidence for the deleterious effects of noise is to make comprehensive

measurements of noise over a large number of genes and look for evidence that noise has

been selected against in certain sets of genes. This was the approach taken by Newman et al.

(2006) and Bar-Even et al. (2006), with the former measuring the noise in expression in over

2500 genes in yeast and the latter examining fewer genes (43) but in a variety of

environmental conditions. Both studies reached strikingly similar conclusions, finding thatnoise stemmed mostly from randomness in mRNA synthesis and destruction and that genes

with higher levels of expression generally exhibited less variability from cell to cell. This

latter point highlights a potential tradeoff between the level of noise in gene expression and

the metabolic cost of maintaining a large number of proteins. They also found that stress

response genes, which are typically non-essential, tended to be noisy, reflecting the potential

benefits of noise in this class of gene (Blake et al., 2006). In contrast, genes involved in

protein synthesis and degradation were much less variable, implying that genes essential for

cellular function require more precise expression levels. The regularity of these essential

genes may be achieved in a number of ways such as genomic positioning of essential genes

in areas of open chromatin that are presumably less noisy (Batada and Hurst, 2007). This

correlative data does not prove the case, however, and an explicit test that noise in essential

genes is deleterious would be fruitful in this regard.

Noise minimization and compensation in gene networks

Given that genes often interact in networks, it is also important to understand how the

effects of noise are minimized in particular genetic networks. To study this more complex

problem, Kollman et al.(2005) in their study of the chemotaxis network of E. coli, began

with several plausible biochemical models of chemotaxis. Each model possessed the



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fundamental property of precise adaptation of pathway activity to local food signals, but

varied in their ability to tolerate noise. Upon measuring the noise and correlations in the

expression of several key components of the pathway, they found that the model that most

successfully tolerated such noise described a network similar to the one found in E. coli,

suggesting that the endogenous network may have evolved to tolerate noise while avoiding

the costs associated with high levels of protein expression.

Another example of noise-resistance in a signaling pathway is the mating pheromoneresponse pathway in yeast studied by Colman-Lerner et al. (2005). Through the use of dual

reporters inspired by Elowitz et al. (2002), they quantified all the different sources of cell-to-

cell variability in their system, with the primary distinction being between random

biochemical events in the propagation of the signal itself and preexisting differences in cells'

capacity to respond to the signal. They found that most of the variability observed was due

to preexisting cellular differences, corroborating other claims that variability in yeast is

largely extrinsic (Raser and O'Shea, 2004; Volfson et al., 2006). Interestingly, though, they

found a surprising negative correlation between the signaling capacity of the pathway in

individual cells and the capacity to express the pathway's target gene in those same cells.

The implication is that variability in the signaling pathway is compensated for at the level of

gene expression, thus allowing the cell to produce a robust gene expression profile despite

large differences in signaling capacities.

Noise resistance has also driven much research into the networks underlying circadian

rhythms, biochemical oscillations present in organisms ranging from cyanobacteria to

humans that are entrained by periodic exposure to sunlight but are capable of free-running

without any external signals. These oscillations display a remarkable fidelity in their

duration from cycle to cycle, but the source of this reliability in still unclear and may depend

on properties of the network used to implement the oscillator. For instance cyanobacteria,

despite possessing perhaps the simplest known clock, produce very regular oscillations.

Notably, the proteins involved can oscillate in vitro in the absence of any transcriptional

regulation at all (Nakajima et al., 2005), but presumably variability in the numbers of these

proteins in individual cells can cause cells to lose synchrony. Indeed, gene expression

variability during the clock cycle has many interesting properties (Chabot et al., 2007).

Another possibility is that cell-cell communication might allow cells to compensate for the

fluctuations in the oscillations of individual cells. This is not the case in cyanobacteria,however, given that when one places two cells at different phases of the circadian cycle next

to each other, their progeny robustly maintain the two different cycles inherited from the

parents (Mihalcescu et al., 2004).

In higher organisms, transcriptional regulation plays a key role in the generation of circadian

rhythms, and single cell experiments have shown the performance of the clock in individual

mammalian cells can be rather poor, with strikingly variable periods observed both in

culture (Nagoshi et al., 2004) and in whole organisms (Liu et al., 1997). There is some

evidence behind the general consensus that cell-cell communication allow all the cells in an

organism's pacemaker to maintain its phase (Liu et al., 1997), but it would be interesting to

explore how noise in gene expression contributes to dephasing individual cells, especially

given recent theories claiming that even these networks seem to have some noise-resistant

properties (Barkai and Leibler, 2000; Forger and Peskin, 2005). More generally, such resultscould apply to other kinds of genetic oscillators like the cell-cycle, where recent work has

shown that noise is a key factor in cell-cycle timing variability (Di Talia et al., 2007).

Noise minimization in development

So far, little work has been done on the role of noise in gene expression in development,

probably due to difficulties in obtaining quantitative measurements. However, one excellent



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example of a developmental buffer against noise is the activity of Hsp90 in Arabidopsis

(Queitsch et al., 2002), the inhibition of which reveals the effects of genetic and

environmental variability. Surprisingly, this same inhibition results in marked

developmental variability even in relatively isogenic populations, most likely stemming

from stochastic effects. An exciting avenue for further research would be to try and link

stochastic gene expression to phenotypic diversity (familiar to geneticists as the common

phenomena of partial penetrance or variable expressivity of phenotypes).

Stochastic gene expression and aging

Another line of evidence that noise is undesirable comes from research showing that aging is

correlated with increased noise in gene expression. In one case, researchers showed that the

expression of a variety of housekeeping and cell-type specific genes in individual murine

cardiac myocytes become increasingly stochastic as the organism aged (Bahar et al., 2006).

They further found that treating cells isolated from young animals with hydrogen peroxide

also produces an increase in expression variability, perhaps indicating that oxidative damage

may be a factor. Similar stochastic effects have been seen in aging murine muscle tissues

(Newlands et al., 1998).

Conversely, the stochastic expression of a gene may actually be responsible for determining

lifespan in C. elegans (Rea et al., 2005). The authors found that the level of expression of a

reporter expressed from a heat shock promoter in response to environmental stress on thefirst day of adulthood was remarkably stochastic and moreover predicted the lifespan of the

organism. Although the mechanisms underlying these stochastic phenomena are still

unclear, it is possible that aging may be surprisingly dependent on the effects of stochastic

gene expression.

Conclusion

We would like to emphasize that despite the flurry of activity in the area of stochastic gene

expression over the last several years, the field is still remarkably young, with many

significant discoveries likely to come in the future. Basic measurements of cell-to-cell

variability in higher eukaryotes are still in their infancy, and single-molecule techniques

have shown that surprises still lurk even in supposedly well-characterized systems such asE.

coli. Moving forward, researchers have also started to examine biological consequences of

noisealready, there are more and more examples of noise being beneficial in isogenic

populations, a trend we expect to continue. We anticipate more studies highlighting how

cells control and tolerate noise to produce reliable behavior. Of course, the most exciting

discoveries are those that are completely unexpected, and given the fundamental nature of

stochastic gene expression, it may prove important in unpredictable ways in experimental

systems both new and old.

Acknowledgments

We would like to thank Michael Laub, Ido Golding, Jim Collins and Jeff Gore for many helpful comments on the

manuscript. We also apologize to any authors whose work we were unable to mention due to space constraints.

A.v.O was supported by NSF grant PHY-0548484 and NIH grants R01-GM068957 and R01-GM077183. A.R. is

supported by NSF Fellowship DMS-0603392 and a Burroughs Wellcome Fund Career Award at the ScientificInterface.

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Figure 1. Intrinsic and extrinsic contributions to noise in gene expressionA) A fluorescence image of individualE. coli displaying marked cell-to-cell variability in

the expression of two identically regulated fluorescent proteins. B) Schematic depiction of

the temporal behaviors of extrinsic noise (upper) and intrinsic noise (lower). C) Expected

cell-to-cell variations when fluctuations are intrinsic, extrinsic or both. (A and B adapted

from Elowitz et al., 2002).



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Figure 2. Noise in prokaryotic gene expression depends on the rates of transcription andtranslation

A) When the transcription rate is high, variability in protein levels is low, but B) when the

transcription rate is lowered and the translation rate is raised, gene expression is far noisier,

even at the same mean, as shown in Ozbudak et al. (2002).



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Figure 3. The Contribution of Transcriptional Bursts to Cell-to-Cell Variability

A) Transcription without bursts with a relatively small amount of noise. B) Bursts in

transcription can cause significantly higher variability, even when producing the same mean

number of transcripts. C) In situ detection of individual mRNA molecules reveals large cell-

to-cell variability in mammalian cells. D) Experimental histogram of mRNA numbers. The

grey dashed line depicts the theoretical distribution one would expect in the absence of

transcriptional bursts. (C and D adapted from Raj et al., 2006)



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